This package provides Autograd-compatible approximations to the gamma family of functions.

ML Collections is a library of Python collections designed for Machine Learning usecases.

This library is used internally as header-only library by PyTorch.

Pomegranate is a graphical models library for Python, implemented in Cython for speed.

This package is a high-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model, implemented in plain C/C++ without dependencies, with

• AVX intrinsics support for x86 architectures

• VSX intrinsics support for POWER architectures

• Mixed F16 / F32 precision

• 4-bit and 5-bit integer quantization support

• Zero memory allocations at runtime

• Support for CPU-only inference

• Efficient GPU support for NVIDIA

• OpenVINO Support

• C-style API

LIBSVM is a machine learning library for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data.

Captum is a model interpretability and understanding library for PyTorch. Captum contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models. It has quick integration for models built with domain-specific libraries such as torchvision, torchtext, and others.

GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. GPy implements a range of machine learning algorithms based on GPs.

OpenMM is a toolkit for molecular simulation. It can be used either as a stand-alone application for running simulations, or as a library you call from your own code.

KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, builds off llama.cpp and adds many additional features:

• Runs on CPU or GPU, supports full or partial offloaded

• LLM text generation (Supports all GGML and GGUF models, backwards compatibility with ALL past models)

• Image Generation (Stable Diffusion 1.5, SDXL, SD3, Flux)

• Speech-To-Text (Voice Recognition) via Whisper

• Text-To-Speech (Voice Generation) via OuteTTS, Kokoro, Parler and Dia

• Provides many compatible APIs endpoints for many popular webservices (KoboldCppApi OpenAiApi OllamaApi A1111ForgeApi ComfyUiApi WhisperTranscribeApi XttsApi OpenAiSpeechApi)

• Bundled KoboldAI Lite UI with editing tools, save formats, memory, world info, author's note, characters, scenarios

• Includes multiple modes (chat, adventure, instruct, storywriter) and UI Themes (aesthetic roleplay, classic writer, corporate assistant messsenger)

• Supports loading Tavern Character Cards, importing many different data formats from various sites, reading or exporting JSON savefiles and persistent stories

• Many other features including new samplers, regex support websearch, RAG via TextDB, image recognition/vision and more

All up-to-date GGUF models are supported, and KoboldCpp also includes backward compatibility for older versions/legacy GGML models.

PyTorch Lightning is just organized PyTorch; Lightning disentangles PyTorch code to decouple the science from the engineering.

This package provides common Python utilities and GitHub Actions for the Lightning suite of libraries.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

BoTorch is a library for Bayesian Optimization built on PyTorch.

fastText is a library for efficient learning of word representations and sentence classification.

Lantern provides a C API to the libtorch machine learning library.

Brian is a simulator for spiking neural networks written in Python. It is therefore designed to be easy to learn and use, highly flexible and easily extensible.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

This package provides a C++ and Python library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.

Not all possible optimizations can be directly implemented on ONNX graphs--- some will need additional backend-specific information---but many can, and the aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.

frugally-deep is a header-only C++ library for inference of Keras machine learning models on a single CPU core.

LIBSVM is a machine learning library for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

Vowpal Wabbit is a machine learning system with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Kaldi is an extensible toolkit for speech recognition written in C++.